CI/CD
About Workato
Workato is the leading Control and Execution Platform for Enterprise AI — the neutral platform enterprises trust to put AI to work across their business. Workato unifies data, applications, and processes into a single platform so AI can reliably orchestrate business processes in production at enterprise scale. Built on more than a decade of running mission-critical processes for over half the Fortune 500 — including Nasdaq, Amazon, Cisco, Vodafone, Atlassian, and Lucid Motors — Workato turns over 14,000 enterprise systems AI needs to act on into one governed execution layer. For more information, visit workato.com.
Why join us?
Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles. We are driven by innovation and looking for team players who want to actively build our company.
But, we also believe in balancing productivity with self-care. That’s why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives.
If this sounds right up your alley, please submit an application. We look forward to getting to know you!
Also, feel free to check out why:
• Business Insider named us an “enterprise startup to bet your career on”
• Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world
• Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America
• Quartz ranked us the #1 best company for remote workers
Responsibilities
You’ll lead Analytics Engineering and Product Analytics at Workato. Your team will help us understand how enterprises adopt AI and orchestration, where promising projects stall, and what changes when customers expand from one successful deployment to many. You’ll work directly with Product leadership, guide the engineers who make usage data reliable, and develop the analysts who turn that data into decisions and original industry research.
What you’ll own
Data strategy and delivery
• Set the strategy, roadmap, priorities, and capacity plan for both teams. Balance investments in shared data foundations with time-sensitive product decisions and investigations the team initiates.
• Define who owns product instrumentation, pipelines, datasets, metrics, and analysis. Agree with Product and Engineering on handoffs and who makes technical and analytical decisions.
• Turn major requests into scoped initiatives with owners, milestones, and success criteria. Resolve dependencies across Product, Engineering, Infrastructure, and Security. Bring progress, risks, and resource trade-offs to leadership early enough to act.
People and organization
• Own hiring, onboarding, performance management, and workforce planning. Identify the skills and leadership capacity the organization needs as Workato’s portfolio grows.
• Set clear expectations and career paths for managers, analytics engineers, and product analysts. Give engineers ownership of architecture and reliability. Coach analysts to frame a question, challenge an assumption, and defend a recommendation with evidence.
Analytics engineering and the data platform
• Lead the engineering work to collect, model, transform, and curate product usage data. Deliver analytics-ready datasets that connect builder activity, workspaces, customer accounts, deployments, and production workloads.
• Partner with senior engineers on architecture, orchestration, and tooling. Weigh scalability, maintainability, cost, and delivery speed when deciding whether to extend a data model, build a pipeline, or change the platform.
• Establish standards for automated testing, CI/CD, documentation, lineage, and pipeline monitoring. Give critical datasets an owner and a way to detect stale, missing, or incorrect data before it affects a decision.
Product analytics and experimentation
• Bring analysts into roadmap discussions while options are open. Understand the choices a product leader faces, test competing explanations, and identify what evidence would change the recommendation.
• Define measurement during feature planning. Agree with PMs and engineers on the intended customer outcome, what telemetry to capture, who will implement it, and how to validate it before launch. Review the results after release.
• Measure activation, engagement, retention, and expansion. Show whether growth comes from more customers reaching production, more business processes per customer, or greater volume in existing workloads. Each finding points to a different product decision.
• Lead experiment design and interpretation. Define hypotheses, success metrics, and guardrails. Use funnels, cohorts, A/B tests, and holdouts where appropriate. Explain what the team can conclude when enterprise sample sizes are small.
• Match the analysis to the stage of the product. Identify early signs of customer value for new offerings. Forecast adoption and growth for established products. Recommend where to invest, what to change, and when to measure again.
Metrics, self-service, and AI-assisted analytics
• Publish a shared catalog of product and business metrics. Document each metric’s definition, calculation, source, and owner. Verify that the data platform, dashboards, and reports implement it consistently.
• Give analysts and business teams governed datasets and reusable tools for reporting, experimentation, and advanced analysis. Make recurring questions easy to answer without a new engineering request.
• Develop AI-assisted analytics that can monitor metrics, investigate changes, and explain findings. Ensure answers respect access permissions and let users verify the source and calculation.
Data culture and industry research
• Establish regular reviews where Product, Sales, and Customer Success discuss a usage pattern, decide what to do, and return to see whether it worked. Help field t